Consumption-driven forecasting using multi-level heterogeneous input data

ABSTRACT

A method, system, and computer program product for generating forecasts and replenishment plans. Some embodiments commence upon receiving point-of-sale data, then receiving distribution-level order data in a second data format. The first point-of-sale data comprises an item identifier and a first date or first date range, and the distribution-level order data comprises the item identifier and a second date or second date range. The originators of the order data are determined using address identifiers (e.g., network location identifiers). The received data is combined wherein at least a portion of the point-of-sale data is combined with at least a portion of the distribution-level order data to generate a combined forecast for the item. Further processing includes receiving an inventory model parameter and combining at least a portion of the first point-of-sale consumption data with at least a portion of the distribution-level order data to generate a replenishment plan for the item.

RELATED APPLICATIONS

The present application is related to co-pending U.S. patent applicationSer. No. ______, (Atty. Docket ID ORA140885-US-NP) entitled “USINGCONSUMPTION DATA AND AN INVENTORY MODEL TO GENERATE A REPLENISHMENT PLANfiled on even date herewith, which is hereby incorporated by referencein its entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The disclosure relates to the field of forecasting, stocking andreplenishment systems and more particularly to techniques for generatingaccurate supplier forecasts.

BACKGROUND

Suppliers (e.g., manufacturers) and purveyors (e.g., stores,clearinghouses, warehouses, etc.) need to share data. For example,sellers need to order commodities (e.g., items to be sold and/orconsumed) with enough precision and certainty that suppliers can respondto the orders with commensurate precision and certainty. Historically,suppliers have dealt with incoming orders using ad hoc techniquesinvolving many ad hoc data formats. In legacy cases, each orderingentity (e.g., a wholesaler, a distribution center, etc.) of productsfrom a particular supplier would provide orders to the supplier, and thesupplier would forecast on the basis of received orders. However, such alegacy handling of forecasting based on orders received from entities inthe distribution tiers fails to account for point of sale data. Failureto account for point-of-sale data can cause a “bullwhip effect” where alack of visibility to the point-of-sale demand causes demand variationsto become amplified as these variations propagate upstream through thedistribution chain to the supplier. Overstatements and understatementsof quantities in upstream forecasts (e.g., when the order quantities aregreater or less than actually called for by the variations in realconsumer demand) tend to result in volatility in the form of inventorybuild-up and/or shortages at various points in the distribution tiers.What's needed is a technique or techniques to produce accurate forecastsby combining point-of-sale ordering data with ordering data fromentities in the distribution tiers.

None of the aforementioned legacy approaches achieve the capabilities ofthe herein-disclosed techniques for generating consumption-drivenforecasts using multi-level heterogeneous input data. Therefore, thereis a need for improvements.

SUMMARY

The present disclosure provides an improved method, system, and computerprogram product suited to address the aforementioned issues with legacyapproaches. More specifically, the present disclosure provides adetailed description of techniques used in methods, systems, andcomputer program products for dynamic time-phased consumption-drivenforecasting and replenishment planning.

Some embodiments commence upon receiving point-of-sale data in a firstdata format, then receiving distribution-level order data in a seconddata format. The first point-of-sale data comprises an item identifierand a first date or first date range; and the distribution-level orderdata comprises the item identifier (e.g., for the same item) and asecond date or second date range. The originator of the order data isdetermined using address identifiers (e.g., network locationidentifiers). The received data is combined wherein at least a portionof the point-of-sale data is combined with at least a portion of thedistribution-level order data to generate a combined forecast for theitem.

Further details of aspects, objectives, and advantages of the disclosureare described below and in the detailed description, drawings, andclaims. Both the foregoing general description of the background and thefollowing detailed description are exemplary and explanatory, and arenot intended to be limiting as to the scope of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A1 depicts an environment showing increasing granularity ofordering from a supplier through distribution channels to multiplepoints of sale.

FIG. 1A2 is a chart showing characteristics of multi-level, multi-formatdata exchanges as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 1A3 is a multi-level, multi-format example of a planning engine asused in systems for dynamic time-phased consumption-driven forecastingand replenishment planning, according to some embodiments.

FIG. 1B1 depicts a planning scenario that combines point-of-sale dataand inventory data as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 1B2 is a system for combining point-of-sale data and inventory dataas used to implement dynamic time-phased consumption-driven forecastingand replenishment planning, according to some embodiments.

FIG. 2A depicts a data flow for combining multi-level heterogeneousordering data as used in systems for dynamic time-phasedconsumption-driven forecasting planning, according to some embodiments.

FIG. 2B depicts a data flow for combining point-of-sale data andinventory data as used to implement dynamic time-phasedconsumption-driven planning, according to some embodiments.

FIG. 2C depicts a data flow for combining point-of-sale data, inventorydata, and historical data to form a time-phased safety stock plan,according to some embodiments.

FIG. 3A and FIG. 3B depict a data structure for combining multi-levelheterogeneous ordering data as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning.

FIG. 4A presents a case study showing the effects of a 95% service levelsafety stock strategy as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 4B presents a data flow for selecting a safety stock replenishmentstrategy as used in dynamic time-phased consumption-driven forecastingand replenishment planning, according to some embodiments.

FIG. 4C presents a data flow for generating a replenishment plan basedon a safety stock replenishment strategy as used in dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 5A is a user interface showing characteristics of a lead-timesafety stock replenishment strategy as used in dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 5B is a user interface showing characteristics of a supply-orientedsafety stock replenishment strategy as used in dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 6 is a block diagram of a system for dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 7 is a block diagram of a system for dynamic time-phasedconsumption-driven forecasting and replenishment planning, according tosome embodiments.

FIG. 8 depicts a block diagram of an instance of a computer systemsuitable for implementing embodiments of the present disclosure.

DETAILED DESCRIPTION

Disclosed herein and in the accompanying figures are exemplaryenvironments, methods, and systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning.

Overview

Sales and order data from point-of-sale locations and from other pointsin a supply-to-consumer channel can vary with successively morevolatility as time goes on, and suppliers and manufacturers may beimpacted from such volatility. A ‘bullwhip’ effect tends to createoverages in inventory followed by shortages in inventory. One approachto ameliorate the bullwhip effect is to account for point-of-sale datawhen producing demand forecasts provided to upstream distribution andwarehousing entities. Accounting for point-of-sale data when rolling upforecasts for suppliers serves to reduce bullwhip effect volatility inorders (e.g., overstatements). Further, accounting for point-of-saledata when rolling up forecasts for suppliers serves to reduce the cycleof inventory build-up followed by shortages at various points in thedistribution tiers.

Suppliers need accurate forecasting of demand in order to plan forfuture deliveries—so as to keep the items in stock for immediate “offthe shelf” purchase or “fast delivery” to satisfy consumer demands. Suchprocessing of forecast errors can have fairly long range consequences,for example when the supplier significantly over-orders raw materials.Suppliers want forecasts that exhibit a high degree of fidelity to theactual materials and/or product movement (e.g., accurate roll-upforecasts), and point-of-sale operators want deliveries that exhibit ahigh degree of fidelity to the timing and quantity of units that thepoint-of-sale operators will need to satisfy demand. Issues ofvolatility can be solved by combining point-of-sale demand data withdistribution tier demand data. Embodiments of the present disclosureinclude methods and systems that serve to:

-   -   Generate orders for suppliers that combine orders from        point-of-sale operators (e.g., retail outlets), with orders from        other operators or carriers in the distribution channel (e.g.,        from wholesalers or from distributor's, or from distribution        centers, etc.) so as to generate forecasts that dampen out or        eliminate the bullwhip effect.    -   Maintain a repository of inventory model parameters (e.g., goal        parameters), including service-level quantification that can be        used when forming a replenishment plan,    -   Deploy a computer-implemented engine that can calculate a        detailed and accurate (e.g., accurate in terms of timing and        accurate in terms of order size) replenishment plan based on        combinations of received point-of-sale consumption data,        distribution tier orders, and inventory models.

Definitions

Some of the terms used in this description are defined below for easyreference. The presented terms and their respective definitions are notrigidly restricted to these definitions—a term may be further defined bythe term's use within this disclosure.

-   -   The term “exemplary” is used herein to mean serving as an        example, instance, or illustration. Any aspect or design        described herein as “exemplary” is not necessarily to be        construed as preferred or advantageous over other aspects or        designs. Rather, use of the word exemplary is intended to        present concepts in a concrete fashion.    -   As used in this application and the appended claims, the term        “or” is intended to mean an inclusive “or” rather than an        exclusive “or”. That is, unless specified otherwise, or is clear        from the context, “X employs A or B” is intended to mean any of        the natural inclusive permutations. That is, if X employs A, X        employs B, or X employs both A and B, then “X employs A or B” is        satisfied under any of the foregoing instances.    -   The articles “a” and “an” as used in this application and the        appended claims should generally be construed to mean “one or        more” unless specified otherwise or is clear from the context to        be directed to a singular form.

Reference is now made in detail to certain embodiments. The disclosedembodiments are not intended to be limiting of the claims.

Descriptions of Exemplary Embodiments

FIG. 1A1 depicts an environment showing increasing granularity ofordering from a supplier through distribution channels to multiplepoints of sale. As an option, one or more instances of environment 1A100or any aspect thereof may be implemented in the context of thearchitecture and functionality of the embodiments described herein.Also, the environment 1A100 or any aspect thereof may be implemented inany desired environment.

As shown in FIG. 1A1, quantities (e.g., quantities of products) movefrom suppliers (e.g., suppliers 102) to various points of sale 128. Themovement of products may come in the form of bulk shipments 104, whichshipments may be stored in an intermediate staging and/or storagefacility (e.g., see the materials in-storage facility and/or in-transitfacility 114).

Continuing with the discussion of this FIG. 1A1, quantities that arestored and/or are in transit may become bulk deliveries 116, which canbe delivered to distribution centers 122. The distribution centers 122serve as a repository for quantities which become deliveries 124, whichin turn are to be delivered to various points of sale 128. The shownpoints of sale may include brick-and-mortar stores, kiosks or onlinestores. For example stores may include store_A 130, store_B 132, a kiosk134, and or a holding facility for an online store 136. In some casesquantities are delivered directly from a state of being in-transitdirectly to points of sale 128 (e.g., without being stored at adistribution center).

When a store or kiosk is in need of additional quantities (e.g., toreplenish stock), the store or kiosk might place an order (e.g., ordersfrom points of sale 126) to a supplier. However in some cases, apoint-of-sale demand for additional quantities may be serviced by adistribution center (e.g., distribution centers 122). In such a case,the distribution center may place an order to a supplier. Certain othercases exist where an order is placed to a supplier. As shown, an agentfor materials in storage and or an agent handling materials that arein-transit may place orders (e.g., orders from transit 112) to thesupplier to cover quantities damaged in storage or transit. Another casecovers the situation where an agent or sub-manufacturer dealing withpartially complete materials may place orders to a supplier (e.g., tocover defective materials).

As can be seen, suppliers 102 receives orders at multiple times, andfrom multiple agents or entities. As such, those supplier needs atechnique where orders can arrive from points of sale, distributionfacilities or warehouses, materials and storage facilities and/or fromin-transit facilities, or even from sub-manufacturer's, and yet thesupplier is able to receive or reconcile and process these orders, eventhough the orders are raised by many different agents or entities in theecosystem. In addition to the foregoing complexity, orders raised fromone agent or entity might refer to quantities differently than ordersraised from another agent or entity. For example, orders from points ofsale 126 might refer to quantities in relatively small units (e.g.,individual units, or individual small packets), whereas orders raisedfrom a distribution center might refer to quantities in relativelylarger units (e.g., pallets or cartons). Similarly, the time framesreferring to the need for orders to be fulfilled might varysubstantially, depending on the nature of the agent or entity. Strictlyas one example, a point-of-sale agent or entity might raise orders basedon a forecast through an upcoming month. Or, a distribution center agentor entity might raise orders based on a forecast through an upcomingquarter, or even longer period of time. As shown, the granularity ofquantities and time frames increases through the progression fromsupplier to points of sale.

As time progresses, additional agents or entities may enter into theecosystem. In some cases an additional agent or entity creates yet anadditional level of distribution. Any addition of an agent or entityand/or any addition of a level of distribution between supplier andpoints of sale may introduce the potential for additional data formats,according to which data formats orders are placed. As such orders may bedelivered to suppliers in accordance with a range of data formats, whichrange of data formats and correspondence to distribution levels arepresently discussed.

FIG. 1A2 is a chart 1A200 showing characteristics of multi-level,multi-format data exchanges as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning. As an option,one or more instances of chart 1A200 or any aspect thereof may beimplemented in the context of the architecture and functionality of theembodiments described herein. Also, the chart 1A200 or any aspectthereof may be implemented in any desired environment.

FIG. 1A2 depicts the situation where multiple levels of distributionraise orders to suppliers using various formats of order data. As thenumber of agents and entities increases, so does the range of dataformats 159. Further the range of data formats may vary across a singledistribution level, or the range of data formats 159 may be different(or be common) from one distribution level into a second distributionlevel.

One approach to handling such a wide range of data formats, possiblyencompassing multiple levels of distribution, is to rely on thesuppliers to reconcile order data even though the order data isheterogeneous and possibly also encompassing multiple levels ofdistribution. The following FIG. 1A3 depicts such an approach.

FIG. 1A3 is a multi-level, multi-format example of a planning engine1A300 as used in systems for dynamic time-phased consumption-drivenforecasting and replenishment planning. As an option, one or moreinstances of planning engine 1A300 or any aspect thereof may beimplemented in the context of the architecture and functionality of theembodiments described herein. Also, the planning engine 1A300 or anyaspect thereof may be implemented in any desired environment.

As shown, a single instance 115 is used for combining data from variouspoints of sale (e.g., store_A), and to provide an ordering forecast to aparticular supplier or manufacturer (e.g., MFG_P, MSG_Q, MFG_R, etc.)This architecture supports processing orders from any combinations oforders from stores and orders from other points in the distributionnetwork, if such orders are available. For example manufacturer MFG_Pmight receive an order forecast generated by combining data from stores(e.g., store_A), and from other points within the distribution networksuch as from a vendor-managed inventory point (e.g., VMI_B), and/or fromwholesalers and/or distributors (e.g., disty_C).

The architecture of FIG. 1B1 includes a system comprising a singleinstance 115 (see consumption driven planning engine 152) that isconfigured to accept order data in multiple formats, and is furtherconfigured to deliver forecasts to manufacturers in any or one or moreforecast formats. As shown, forecasts from a store (see store_A), avendor-managed inventory location (see VMI_B), and a distributor(disty_C) are received by the consumption driven planning engine 152,which in turn employs a forecasting and ordering module 154.Theaforementioned forecasting and ordering module is able to compile aforecast that can be broken down into quantities pertaining to the itemsin the forecast that are handled by a corresponding supplier. As shown,a consolidated forecast is delivered to MFG_P, MFG_Q, and MFG_R. Thesemanufacturers receive such orders, produce the ordered goods, andorganize deliveries. The deliveries may then be provided to any agent orentity serving the function of replenishment functions 158 (e.g.,storage, consolidation, just-in-time delivery, etc.). In some cases, thesuppliers/manufacturers build to the consolidated forecast, and producegoods that are moved through a distribution network in accordance with areplenishment plan.

Various techniques to construct a replenishment plan can be used by thereplenishment plan generator 157. In particular, the architecture ofFIG. 1A3 supports techniques that use consumption data (e.g., POS data)and an inventory model 163 to generate a replenishment plan. The shownembodiment generates a replenishment plan based on an inventory model163 (e.g., including a service level model, including a safety stockstrategy), and a stock level calculator 156. A safety stock strategy canbe selected using a strategy selector 161, as shown.

As such, the architecture of FIG. 1A3 supports a technique where acomputing resource can receive point-of-sale data in a first data format(e.g., comprising item identifier, a date or date range and an originidentifier, etc.), and can also receive additional distribution-leveldata in a second data format (e.g., comprising the item identifier in adifferent representation, a date or date range and an origin identifier,etc.),. The computing resource (e.g., configured to executeconsumption-driven planning engine) can then combine the firstpoint-of-sale data from a first entity (e.g., Entity_1, such asMallMart, Inc.) with distribution-level data from a second entity (e.g.,Entity_2 and/or Entity_3, such as SysKo, Inc.) to generate a combinedforecast, which in turn can be used to generate a replenishment plan.

Further steps can be performed, such as formatting the combined forecastinto a data format convenient for the manufacturers or other recipients.In some cases, orders can be manipulated into a single common format,however the source of the orders—even though in a common format—oftenimpacts and or determines the interpretation of such orders. Asheretofore mentioned, an agent or entity acting in a wholesalingcapacity might order on the basis of historical order quantities and/orprojected/forecasted time frames and/or lead times. In some situationsan agent or entity acting in a wholesaling capacity might order onbehalf of a large number of the wholesaler's customers, sometimesover-ordering in the hope of meeting any spikes in demand that are notreflected in the historical order quantities. This technique issometimes considered in the calculation of “safety stock”. Orderingsafety stock can be effective in some situations, however in somesituations the stock is expensive and/or perishable, and in manysituations the ordering of safety stock tends to induce unwanted effectssuch as undesirable financial effects (e.g., resulting from carryingcharges) and/or unwanted operational effects (e.g., excess stock onhand, spoiled or expired goods, etc.). Techniques that combine wholesaleordering techniques (e.g., ordering based on distribution centerforecasts and/or inventory levels) with retail-level ordering techniques(e.g., ordering based on point-of-sale data) can be employed to improveaccuracy and avoid a range of unwanted effects. One such situation isdescribed and addressed as pertains to the following FIG. 1B1.

FIG. 1B1 depicts a planning scenario 1B 100 that combines point-of-saledata and inventory data as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning. As an option,one or more instances of planning scenario 1B100 or any aspect thereofmay be implemented in the context of the architecture and functionalityof the embodiments described herein. Also, the planning scenario 1B100or any aspect thereof may be implemented in any desired environment.

The problems associated with coarse, and sometimes very inaccurateforecasting by wholesale channels, are solved by including retail-leveldetailed historical sales data and retail-level forecasts whengenerating replenishment and inventory quantities. Some systems employdata collection facilities (e.g., point-of-sale barcode readers) as wellas use of stored raw and calculated data (e.g., previous sales figures,previous forecasts, calculated average on-shelf amounts, peak andminimum daily sales figures, etc.).

As shown in FIG. 1B1, a consumption driven planning engine 152 receivespoint-of-sale data 141 as well as inventory data 143. The point-of-saledata 141 and the inventory data 143 derive from different sources, eachof which different source uses different techniques for collecting anddisseminating the data. For example, and as shown, point-of-sale data141 can come in the form of store-level data at the time-wisegranularity of daily sales 164 and/or monthly sales 170. In some cases(e.g., see “Store_A”) the daily sales 164 might be collected using onemethod (e.g., register/scanner data), and the monthly sales 170 might becollected using a different method (e.g., monthly on-the-shelf inventorycounts and comparison to monthly order amounts). In other cases (e.g.,see “Store_B”) only daily sales data is collected, and Store_B does notuse any method to collect or disseminate monthly data. In anothersituation (e.g., “Store_C”), only weekly sales data is collected. Instill other situations (e.g., see “Store_D”) all of daily, weekly andmonthly sales data are collected for analysis and dissemination. In manycases, the technique used for collecting instances of point-of-sale data141 and the technique used for collecting instances of inventory data143 may be different, and in some cases may depend on the nature of theproviding point-of-sale entity. For example, “Store_A” uses “Format_A”,“Store_B” uses “Format_B”, “Store_C” uses “Format_C”, and so on.

The origin of such different inputs and/or different formats can bedetermined by the shown consumption driven planning engine 152. Forexample, the origin of the received point-of-sale data can be determinedusing an address identifier or address label (e.g., a label attached toor within a data container), an IP address (e.g., possibly including aport), a network identifier (e.g., a wireless SSID), and/or a URL (e.g.,that identifies the location of the sender, or location of one or moreapplication programming interface calls). In some cases a differentinput channel and/or application programming interface is used for eachformat. The aforementioned constituents of an address identifier can beused singly or in combination to form an address identifier sufficientto identify the origin. In some cases, an address identifier canidentify not only the origin, but also the originator.

As earlier indicated, the practice of over-ordering (e.g., by wholesalechannels and/or distribution channels) to amass inventory, “just incase” still sometimes fails to mitigate the potential for lost salesduring periods of higher demand Further as earlier indicated, amassingthe excess stock may be expensive or may be impractical.

A data preprocessor 186 and a predictor 182 can be configured to receivepoint-of-sale data, to identify the preparer or sender or the data, toresolve the received point-of-sales data to a daily forecast (or hourlyforecast or any other time-period) and confirm that the inventory data143 supports the time-sequenced demand given in the point-of-sale data.In some cases, the inventory can be predicted to be too low (e.g.,relative to a period of high demand from the retail outlets), and apredictor 182 might increment an existing order, or might issue asupplemental order to the manufacturer of the items that are drawn downduring periods of high demand from the retail outlets. In some cases,and as shown in FIG. 1B1, the predictor 182 includes a time-wise andquantity-wise model for ordering, and an additional module processes thetime-wise and quantity-wise model to generate one or more orders thatcomport with the order formatting and submission characteristics as maybe specified by the various manufacturers.

In exemplary cases, the volume of data comprised by the point-of-saledata 141 and the inventory data 143 is huge, and orders are processed bya computer and provided to the manufacturer as computer-readable dataitems. Often, the orders themselves are huge, and are transmitted froman order management system 184 to one or more computer systemsadministrated by the one or more manufacturers. The manufacturers inturn use such data in their own procurement systems so as to procureenough raw materials and/or partially-finished goods be able tomanufacture enough quantities, and in time to satisfy the flow oforders. In such exemplary cases, a system such as pertains to FIG. 1B2is deployed. Use of such a system is now briefly discussed.

FIG. 1B2 is a system 1B200 for combining point-of-sale data andinventory data as used to implement dynamic time-phasedconsumption-driven forecasting and replenishment planning. As an option,one or more instances of system 1B200 or any aspect thereof may beimplemented in the context of the architecture and functionality of theembodiments described herein. Also, the system 1 B200 or any aspectthereof may be implemented in any desired environment.

The shown system 1B200 includes a database engine 198, which includes apersistent storage system 196. Such a persistent storage system servesas a repository for point-of-sale data (e.g., daily sales 164, weeklysales 176, monthly sales 170, etc.), as well as for inventory data(e.g., data from wholesale channels and/or distribution channels 188).The aforementioned data can be stored in any format (e.g., in tablesand/or in files), and any data needed by the consumption driven planningengine 152 and/or its constituent components (e.g., data preprocessor186, predictor 182, etc.) can be delivered in forms used by relationaldatabase systems. Strictly as one example, data stored in files can beconverted into tables or relations by the shown relational datamanagement facility 194 ₁. Moreover, data stored in relations can beconverted into tables by the shown relational data management facility194 ₂.

The data can be stored in any time-wise format, and can be convertedinto any other time-wise format using any known technique. For example,point-of-sale data and/or inventory data might be received and stored ina highly granular time-wise data format (e.g., levels by day, or levelsby hour) and can be converted into a less granular time-wise data format(e.g., levels by week). Or, in other situations, for example,point-of-sale data and/or inventory data might be received and stored ina coarse time-wise data format (e.g., levels by week, or levels bymonth) and can be converted into a highly granular time-wise dataformats (e.g., levels by day or by hour). The received data can bestored persistently, and thus a history can be assembled. In some cases,a calendar is superimposed over a historical period, and store sell-inor channel sell-through data can be recognized and/or tagged ascorresponding to a holiday or as corresponding to seasonal periods.Further, a calendar is superimposed over a forecast period, and apredicted days-of-supply and/or store lead-time can be calculated andused by an order management and/or by a manufacturing procurementsystem.

FIG. 2A depicts a data flow 2A00 for combining multi-level heterogeneousordering data as used in systems for dynamic time-phasedconsumption-driven forecasting. As an option, one or more instances ofdata flow 2A00 or any aspect thereof may be implemented in the contextof the architecture and functionality of the embodiments describedherein. Also, the data flow 2A00 or any aspect thereof may beimplemented in any desired environment.

As shown in FIG. 2A, a single instance 115 comprising an incoming dataprocessing module 297 receives a first set of point-of-sale data in afirst data format (e.g., point-of-sale data 141 ₁, point-of-sale data141 ₃, point-of-sale data 141 _(N-1), etc.). The first set ofpoint-of-sale data comprises dates and/or date ranges and describessales of one or more particular items (e.g., via a SKU number or otheritem identifier). The incoming data processing module can know theorigin of the data from the communication link and/or can know theorigin of the data based on a transmitted first point of saleidentifier. The point-of-sale data refers to an item 117 or items soldon a given date or dates and/or sold within specified date ranges (e.g.,daily sales 164, weekly sales 176, monthly sales 170, etc.).

Also, the single instance 115 is capable of receiving data fromdistribution channels (e.g., distribution channel data 188 ₁,distribution channel data 188 _(N), etc.). The data from thedistribution channels may be stored and/or transmitted in a first dataformat as described above, or the distribution channel data may bestored and/or transmitted in a second data format. Even when the seconddata format is very different from the first data format, the itemidentifiers and/or dates and/or date ranges the meanings may be thesame.

Data can be received in, retained in, and output in any known timeformat or granularity. Any known techniques can be used to convertbetween time formats and granularities, and such techniques can be usedto reconcile dates and/or date ranges as may be received within thefirst and second point of sales data. An instance of a datapreprocessors 186 ₀ serves to receive at least a portion of the firstpoint-of-sale data with at least a portion of the distribution channeldata to generate a combined forecast 223. The combined forecast can beformatted and/or re-formatted into a reformatted combined forecast 225suited to be sent to one or more suppliers. In some cases, data can bestored in a first data table to contain store-level demand data (e.g.,for a particular customer or entity) and in a second data table tocontain aggregate data (e.g., DC level data for another customer orentity). In such a scenario, and for a given product and time period(e.g., week, month, quarter, etc.), a combined forecast is deemed to bethe sum of DC-level demand plus store-level demand

In the embodiment shown, an outgoing data processing module 298 servesto generate a reformatted combined forecast. The reformatted combinedforecast comprises a forecasted quantity of items to be manufacturedand/or delivered by a certain date (e.g., the POS needs the quantity tobe delivered next Saturday) or the reformatted combined forecastcomprises a quantity to be manufactured and/or delivered at some datewithin a particular date range (e.g., the POS need to process aparticular quantity of incoming stock for within the week of December10-December 17). Strictly as examples, forecasts can be provided astables, and may cover any time range (e.g., daily forecast 264, weeklyforecast 276, monthly forecast 270, etc.).

Once generated, the outgoing data processing module 298 sends (e.g.,transmits) the reformatted combined forecast over a data link. Further,at any point in time, the incoming processing system and/or the outgoingprocessing system can retrieve and/or store data from/to the persistentstorage system 196. The persistent storage system 196 can storepoint-of-sales data from any source, including instances ofbrick-and-mortar stores, instances of kiosks, and/or instances of onlinestores, etc.

FIG. 2B depicts a data flow 2B00 for combining point-of-sale data andinventory data as used to implement dynamic time-phasedconsumption-driven planning. As an option, one or more instances of dataflow 2B00 or any aspect thereof may be implemented in the context of thearchitecture and functionality of the embodiments described herein.Also, the data flow 2B00 or any aspect thereof may be implemented in anydesired environment.

As shown in FIG. 2B, a single instance 115 hosts an incoming dataprocessing module 297, which is configured to receive point-of-sale data(e.g., point-of-sale data 141 ₁, data 141 _(N), etc.) from variouspoints of sale (e.g., instances of brick-and-mortar stores, instances ofkiosks, and/or instances of online stores, etc.). The incoming dataprocessing module 297 is further configured to receive inventory datafrom one or more agents or entities within the distribution-levelecosystem. The aforementioned inventory data (e.g., inventory levels 177₁, inventory levels 177 _(N), etc.) includes an identifier of an item117 or multiple identifiers of multiple items and corresponding dates ordate ranges that characterize the inventory levels for the item or itemson that date or those dates.

In exemplary situations, the incoming data is stored to the persistentstorage system 196 in the granularity as received. When two or moreincoming data items need to be combined, they can be retrieved and thenconverted into a normalized format. Using this technique, only the dataneeded, and furthermore, only the data corresponding to the data neededat a particular moment in time and/or for a particular purpose, need beconverted or normalized.

Continuing, a data preprocessor combines point-of-sale data withinventory data to generate a combined forecast for the item or items. Asshown, an outgoing data processing module 298 is configured with aninstance of time normalizer 190 ₂ and a data preprocessor 186 ₁ toretrieve any portions of combined forecast 223, plus any other data(e.g., data stored at persistent storage system 196) to be combined,normalized, and formatted into time-sequenced inventory andreplenishment plans. The plan can be in any granularity for time (e.g.,daily plan 284, weekly plan 286, monthly plan 288, etc.), and/or theplan can be in any granularity for units (e.g., single items, cartons,cases, pallets, etc.). In exemplary cases, the act of formatting thecombined forecast results in a replenishment plan such as atime-sequenced order summary 227. The time-sequenced order summaryincludes a plan for time-sequenced replenishment of the item or items.In other cases, the time-sequenced order summary 227 includes a plan formaintaining a safety stock of an item, where such a plan comprises atime-sequenced plan for safety stock levels. Aspects of safety stock,stock replenishment strategies, and processing of data to generate asafety stock plan, are discussed in following paragraphs.

FIG. 2C depicts a data flow for combining point-of-sale data, inventorydata, and historical data to form a time-sequenced safety stock plan. Asshown, the single instance 115 receives point-of-sale forecasts (seeoperation 202) and wholesaler's forecasts (see operation 204). Adatastore comprising a history of orders 210 and/or a history ofordering patterns 211 is accessed. An operation (see operation 205)performs various statistical analysis on the history of orderingpatterns 211.

The statistical analysis (e.g., correlation analysis) performed inoperation 205 facilitates identification of ordering patterns, and suchordering patterns might be of a nature that they re-occur. For example,a point-of-sale outlet might sell a large number of strawberry pop-tartsduring the hurricane season. And, since the hurricane season re-occursannually, the annually-recurring pattern of ordering of strawberrypop-tarts emerges from analysis of the ordering history.

In an exemplary situation, the identified historical ordering patterns(such as “last year's hurricane season”) are applied to future timewindows such as “the upcoming hurricane season” (see operation 208).During this or subsequent steps, a data structure can be prepared toorganize items and corresponding quantities into a forecast thatobserves inventory model parameters 213. Inventory model parametersinclude delivery intervals, maximum volume to be delivered at anyinterval, service levels, etc.

The aforementioned forecast can be used to generate a replenishment plan(e.g., a time-sequenced safety stock plan) such that an order for aparticular item 117 can be broken down into quantities and correspondingtime frames so as to, for example, schedule deliveries in accordancewith delivery intervals (see operation 212). In some cases, the timeframes include calculation of lead times (e.g., lead time from placementof the order to the availability at the final point-of-sale location).When a replenishment plan is ready, it can be communicated to suppliersand/or manufacturers (see operation 214).

As can be seen from inspection of FIG. 2C, the point-of-sale forecasts(see operation 202) are distinguished from wholesaler's forecasts (seeoperation 204). One technique to facilitate this distinction is to use adata structure to codify a site. Further, when a site is a point-of-salesite, one designation versus another designation can be codified in adata structure. Such data structures are exemplified in the following.

FIG. 3A and FIG. 3B depict a data structure for combining multi-levelheterogeneous ordering data as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning. As an option,one or more instances of the shown data structures or any aspect thereofmay be implemented in the context of the architecture and functionalityof the embodiments described herein. Also, the data structures or anyaspect thereof may be implemented in any desired environment.

As shown in FIG. 3A, a site (e.g., a manufacturer's site, a supplier'ssite, a distribution center site, a point-of-sale site, etc.) can bemore precisely characterized using data structure 3A00. Specifically,site identifier 301 can hold an account number 304, an account type 306,a site type 308, and a site group 309. Strictly as examples, a siteidentifier 301 might be a unique identifier such as a name; an accountnumber can be an alphanumeric sequence; an account type can refer to anyone of, a manufacturer, a supplier, a distributor, a point-of-saleoperator, etc.; a site type can tag the site as a wholesaler orretailer; and the site group can refer to a taxonomy for a genus ofdifferent species. In some cases, a site identifier 301 further has acorresponding IP address (e.g., possibly including a port), a networkidentifier (e.g., a wireless SSID), and/or a URL.

One case of a site group comprises a point-of-sale group. A member of apoint-of-sale group can be further described using data structure 3B00,which in turn can specify designations such as given by abrick-and-mortar designation 314, a kiosk designation 316, and an onlinestore designation 318. Any of the foregoing data structures and/ordesignations can be used in any of the embodiments disclosed herein.

FIG. 4A presents a case study 4A00 showing the effects of a 95% servicelevel safety stock strategy as used in systems for dynamic time-phasedconsumption-driven forecasting and replenishment planning. As an option,one or more instances of case study 4A00 or any aspect thereof may beimplemented in the context of the architecture and functionality of theembodiments described herein. Also, the case study 4A00 or any aspectthereof may be implemented in any desired environment.

FIG. 4A shows time periods T1, T2, and T3 over a continuous time perioddepicted by the abscissa (x-axis). The origin (y-axis) depicts volume orquantities of an item. Superimposed onto the graph of case study 4A00 isan indication of a distributor's initial replenishment quantity (seepoint 406). The chart depicts depletion of the distributor's initialreplenishment quantity over time (see line segment 408). As shown, thedepletion of the quantity over time is inverse to the ongoing demandline (see line segment 410). At some point in time, the distributor'sinitial replenishment amount becomes depleted (e.g., see the boundarybetween time period T1 and time period T2). At the beginning of timeperiod T2, an amount equal to the distributor's initial replenishmentamount is delivered to replenish the in-store stock, and again ongoingdemand takes down the in-store stock. Given no changes to the scenario,the scenario can re-occur indefinitely without running out of stock.However, in a situation when there is a spike in demand (e.g., asdepicted by spike 416) it is possible that the in-store stock would notbe enough to satisfy the shown spike in demand. This sets up thecondition for a potential out-of-stock situation 414. If the spike indemand were to continue (as shown) the lost sales situation isexacerbated, resulting in the severe out-of-stock situation 412. Thereare many variables, one of which is the specific timing ofreplenishment. For example, if the distributor were to have replenishedjust a short time period earlier that is shown, the potentialout-of-stock situation 414 could have been avoided.

In some cases, and in particular, in cases where historical data isavailable (and when statistics can be performed over the historicaldata), it is possible to amass safety stock to cover demand that thepredictions and/or planning tools did not predict. For example, anincreased replenishment amount and timing for delivery of same can becalculated so as to provide enough stock even over the entirety of thespike in demand with a (for example) “95% certainty” or at a “95%service level”.

Providing a near 100% safety stock might be an appropriate strategy forcertain items (e.g., low-cost non-perishables), however other strategiesmight be employed, for example, when the items to be stocked areexpensive relative to lost opportunities. Embodiments disclosed hereinsupport analysis and selection of various alternative safety stockreplenishment level strategies.

FIG. 4B presents a data flow 4B00 for selecting a safety stockreplenishment strategy as used in dynamic time-phased consumption-drivenforecasting and replenishment planning. As an option, one or moreinstances of data flow 4B00 or any aspect thereof may be implemented inthe context of the architecture and functionality of the embodimentsdescribed herein. Also, the data flow 4B00 or any aspect thereof may beimplemented in any desired environment.

As shown in FIG. 4B, a processing loop 427 includes decisions to be madeif the parameters corresponding to a particular selected safety stockreplenishment strategy result in a safety stock level that is deemed tobe too conservative (see decision 430) or deemed to be too liberal (seedecision 432). The loop can be entered after a trial safety stock levelhas been generated (see operation 426). In some cases a trial safetystock level can be initially generated after analyzing an availableforecast (see operation 420), selecting a safety stock level strategy(see operation 422), and setting parameters based on the selectedstrategy (see operation 424).

As shown, once trial safety stock levels have been generated, theprocessing loop 427 serves to analyze generated safety stock levels andto compare the generated safety stock levels (see operation 428) to anyavailable observations 440 and history (e.g., see history 438). Such ahistory can be the same as, or derived from, the aforementioned historyof ordering patterns, or history 438 can be derived from one or moredifferent sources.

The selection of a safety stock strategy, and/or modifications to safetystock parameters (e.g., to increase levels 434, or to decrease levels436), can be performed iteratively. A replenishment plan can begenerated using the selected safety stock strategy, and selected safetystock parameters.

FIG. 4C presents a data flow 4C00 for generating a replenishment planbased on a safety stock replenishment strategy as used in dynamictime-phased consumption-driven forecasting and replenishment planning,according to some embodiments.

The shown data flow commences upon receiving first point-of-saleconsumption data comprising item identifiers and a date or date range,then receiving distribution-level order data, comprising itemidentifiers referring to at least some of the same items. The data isprocessed by an incoming data processing module (e.g., to reconciledates and account for format variations) before the replenishment plangenerator 157 outputs a replenishment plan 123.

As shown, the stock level calculator accesses inventory model parameters113 (e.g., based on a selected inventory model and/or selectedreplenishment strategy), then combines the point-of-sale consumptiondata with the distribution-level order data to generate a time-phasedreplenishment plan for the identified items (e.g., when a particularquantity of a particular item or set of items should be delivered to aparticular location).

The selection of an inventory model, a safety stock strategy, and/ormodifications to safety stock parameters can be done using a userinterface. One example of such a user interface is shown and discussedas pertaining to the user interfaces of FIG. 5A and FIG. 5B.

FIG. 5A is a user interface 5A00 showing characteristics of a lead-timesafety stock replenishment strategy as used in dynamic time-phasedconsumption-driven forecasting and replenishment planning. As an option,one or more instances of user interface 5A00 or any aspect thereof maybe implemented in the context of the architecture and functionality ofthe embodiments described herein. Also, the user interface 5A00 or anyaspect thereof may be implemented in any desired environment.

In FIG. 5A, a set of items (e.g., Item01, Item02, . . . , Item06) areeach associated with safety stock parameters 501. As shown, the safetystock strategy “SS Lead Time” is selected from a set of alternativesafety stock strategies (see strategy selector menu 502 of FIG. 5B). Thestrategy can use a user-supplied service level 542, which (for example)might be used as a confidence interval. The strategy can also optionallyspecify a service standard error value 544, a days of supply override,and an optional safety stock override. Additional parameters can beprovided, which parameters may depend from the given strategy. Forexample, in the case that the safety stock strategy “SS Lead Time” isselected, then the store-specific target number of days of supply iscalculated (see column 546) as well as other values for display to auser. The safety stock parameters and any overrides are considered incalculations, and a final safety stock value is calculated (see column548).

The foregoing discussion of FIG. 5A includes aspects of the safety stockstrategy selection and “SS Lead Time” (safety stock lead time). However,other safety stock selections are possible, some of which safety stockselections are shown and discussed as pertaining to the following FIG.5B.

FIG. 5B is a user interface 5B00 showing characteristics of a “days ofsupply” safety stock replenishment strategy as used in dynamictime-phased consumption-driven forecasting and replenishment planning.As an option, one or more instances of user interface 5B00 or any aspectthereof may be implemented in the context of the architecture andfunctionality of the embodiments described herein. Also, the userinterface 5B00 or any aspect thereof may be implemented in any desiredenvironment.

As shown in FIG. 5B, the strategy selector menu 502 facilitates userselection of a particular safety stock strategy. The shown choicesinclude, “Safety Stock Days of Supply”, “Safety Stock Fixed Percent”,“Safety Stock Lead Time”, and “Safety Stock Statistical”. With “SafetyStock Days of Supply” safety stock is calculated based on the targetnumber of days of supply to be maintained at the store. With “SafetyStock Fixed Percent” safety stock is calculated based on a percentage ofthe days of supply to be maintained at the store. With “Safety StockLead Time” safety stock is calculated based on the forecast requirementsduring the lead time to ship the product to the store. With “SafetyStock Statistical” safety stock is calculated using the forecast,standard error and the lead time to ship the product to the store. Thisexample depicts the situation where the user selected the “Safety StockDays of Supply” strategy, and the days of supply-based safety stockscreen device 513 depicts the user-defined parameters and calculatedvalues. A particular safety stock replenishment strategy can be selectedusing a screen widget, and a selection can be known on the basis of amenu item value or any other selection technique involving one or moreof, a fixed percent indication, a lead time indication, a service levelindication, or a days of supply indication.

Additional Embodiments of the Disclosure

FIG. 6 is a block diagram of a system for dynamic time-phasedconsumption-driven forecasting. As an option, the present system 600 maybe implemented in the context of the architecture and functionality ofthe embodiments described herein. Of course, however, the system 600 orany operation therein may be carried out in any desired environment. Asshown, system 600 comprises at least one processor and at least onememory, the memory serving to store program instructions correspondingto the operations of the system. As shown, an operation can beimplemented in whole or in part using program instructions accessible bya module. The modules are connected to a communication path 605, and anyoperation can communicate with other operations over communication path605. The modules of the system can, individually or in combination,perform method operations within system 600. Any operations performedwithin system 600 may be performed in any order unless as may bespecified in the claims. The embodiment of FIG. 6 implements a portionof a computer system, shown as system 600, comprising a computerprocessor to execute a set of program code instructions (see module 610)and modules for accessing memory to hold program code instructions toperform: accessing a computing system having a storage subsystem,wherein the storage subsystem comprises at least one database componenthaving a series of records stored on a computer-readable medium, whereinindividual records are accessed using at least a primary key (see module620); receiving point-of-sale data in a first data format, the firstpoint-of-sale data comprising an item identifier and a first date orfirst date range (see module 630); determining a point-of-sale dataorigin using a first address identifier (see module 640); receivingdistribution-level order data in a second data format, thedistribution-level order data comprising the item identifier and asecond date or second date range (see module 650); determiningdistribution-level order data origin data using a second addressidentifier (see module 660); and combining at least a portion of thepoint-of-sale data with at least a portion of the distribution-levelorder data to generate a combined forecast for at least the item (seemodule 670).

FIG. 7 is a block diagram of a system for dynamic time-phasedconsumption-driven planning. As an option, the present system 700 may beimplemented in the context of the architecture and functionality of theembodiments described herein. Of course, however, the system 700 or anyoperation therein may be carried out in any desired environment. Asshown, system 700 comprises at least one processor and at least onememory, the memory serving to store program instructions correspondingto the operations of the system. As shown, an operation can beimplemented in whole or in part using program instructions accessible bya module. The modules are connected to a communication path 705, and anyoperation can communicate with other operations over communication path705. The modules of the system can, individually or in combination,perform method operations within system 700. Any operations performedwithin system 700 may be performed in any order unless as may bespecified in the claims. The embodiment of FIG. 7 implements a portionof a computer system, shown as system 700, comprising a computerprocessor to execute a set of program code instructions (see module 710)and modules for accessing memory to hold program code instructions toperform: identifying a computing system having a storage subsystem,wherein the storage subsystem comprises at least one database componenthaving a series of records stored on a computer-readable medium, whereinindividual records are accessed using at least a primary key (see module720); receiving first point-of-sale consumption data the firstpoint-of-sale consumption data comprising an item identifier and a firstdate or first date range (see module 730); determining a firstpoint-of-sale data origin using a first address identifier (see module740); receiving distribution-level order data, the distribution-levelorder data comprising at least the item identifier and a second date orsecond date range (see module 750); retrieving at least one inventorymodel parameter (see module 760); and combining at least a portion ofthe first point-of-sale consumption data with at least a portion of thedistribution-level order data to generate a replenishment plan for theitem (see module 770).

System Architecture Overview Additional System Architecture Examples

FIG. 8 depicts a block diagram of an instance of a computer system 800suitable for implementing embodiments of the present disclosure.Computer system 800 includes a bus 806 or other communication mechanismfor communicating information, which interconnects subsystems anddevices, such as a processor 807, a system memory 808 (e.g., RAM), astatic storage device (e.g., ROM 809), a disk drive 810 (e.g., magneticor optical), a data interface 833, a communication interface 814 (e.g.,modem or Ethernet card), a display 811 (e.g., CRT or LCD), input devices812 (e.g., keyboard, cursor control), and an external data repository831.

According to one embodiment of the disclosure, computer system 800performs specific operations by processor 807 executing one or moresequences of one or more instructions contained in system memory 808.Such instructions may be read into system memory 808 from anothercomputer readable/usable medium, such as a static storage device or adisk drive 810. In alternative embodiments, hard-wired circuitry may beused in place of or in combination with software instructions toimplement the disclosure. Thus, embodiments of the disclosure are notlimited to any specific combination of hardware circuitry and/orsoftware. In one embodiment, the term “logic” shall mean any combinationof software or hardware that is used to implement all or part of thedisclosure.

The term “computer readable medium” or “computer usable medium” as usedherein refers to any medium that participates in providing instructionsto processor 807 for execution. Such a medium may take many forms,including but not limited to, non-volatile media and volatile media.Non-volatile media includes, for example, optical or magnetic disks,such as disk drive 810. Volatile media includes dynamic memory, such assystem memory 808.

Common forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, or any other magneticmedium; CD-ROM or any other optical medium; punch cards, paper tape, orany other physical medium with patterns of holes; RAM, PROM, EPROM,FLASH-EPROM, or any other memory chip or cartridge, or any othernon-transitory medium from which a computer can read data.

In an embodiment of the disclosure, execution of the sequences ofinstructions to practice the disclosure is performed by a singleinstance of the computer system 800. According to certain embodiments ofthe disclosure, two or more computer systems 800 coupled by acommunications link 815 (e.g., LAN, PTSN, or wireless network) mayperform the sequence of instructions required to practice the disclosurein coordination with one another.

Computer system 800 may transmit and receive messages, data, andinstructions, including programs (e.g., application code), throughcommunications link 815 and communication interface 814. Receivedprogram code may be executed by processor 807 as it is received and/orstored in disk drive 810 or other non-volatile storage for laterexecution. Computer system 800 may communicate through a data interface833 to a database 832 on an external data repository 831. Individualrecords within the database can be accessed using a primary key.

A module as used herein can be implemented using any mix of any portionsof the system memory 808, and any extent of hard-wired circuitryincluding hard-wired circuitry embodied as a processor 807.

In the foregoing specification, the disclosure has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the disclosure. Forexample, the above-described process flows are described with referenceto a particular ordering of process actions. However, the ordering ofmany of the described process actions may be changed without affectingthe scope or operation of the disclosure. The specification and drawingsare, accordingly, to be regarded in an illustrative sense rather than ina restrictive sense.

What is claimed is:
 1. A method comprising: identifying a computingsystem having a storage subsystem, wherein the storage subsystemcomprises at least one database component having a series of recordsstored on a computer-readable medium, wherein individual records areaccessed using at least a primary key; receiving point-of-sale data in afirst data format, the first point-of-sale data comprising an itemidentifier and a first date or first date range; determining apoint-of-sale data origin using a first address identifier; receivingdistribution-level order data in a second data format, thedistribution-level order data comprising the item identifier and asecond date or second date range; determining distribution-level orderdata origin data using a second address identifier; and combining atleast a portion of the point-of-sale data with at least a portion of thedistribution-level order data to generate a combined forecast for atleast the item.
 2. The method of claim 1, wherein the point-of-sale datain a first data format is received from at least one of, a firstbrick-and-mortar store, a first kiosk, and a first online store.
 3. Themethod of claim 1, wherein the distribution-level order data in a seconddata format is received from at least one of, a wholesaler, adistributor, and a distribution center.
 4. The method of claim 1,further comprising formatting the combined forecast into a third dataformat to generate a reformatted combined forecast comprising an itemquantity and a third date or third date range.
 5. The method of claim 1,wherein the second data format is different from the first data format.6. The method of claim 1, wherein the point-of-sale data in a first dataformat is received from a first entity and the distribution-level orderdata in a second data format is received from a second entity.
 7. Themethod of claim 6, wherein the first entity is different from the secondentity.
 8. The method of claim 1, wherein the combined forecast for atleast the item comprises a time range.
 9. The method of claim 1, whereinthe first address identifier comprises a site identifier.
 10. The methodof claim 9, wherein the site identifier comprises at least one of anaccount number an account type, a site type, a site group, acorresponding IP address, a network identifier, and a URL.
 11. Acomputer program product embodied in a non-transitory computer readablemedium, the computer readable medium having stored thereon a sequence ofinstructions which, when executed by a processor causes the processor toexecute a process, the process comprising: receiving point-of-sale datain a first data format, the first point-of-sale data comprising an itemidentifier and a first date or first date range; determining apoint-of-sale data origin using a first address identifier; receivingdistribution-level order data in a second data format, thedistribution-level order data comprising the item identifier and asecond date or second date range; determining distribution-level orderdata origin data using a second address identifier; and combining atleast a portion of the point-of-sale data with at least a portion of thedistribution-level order data to generate a combined forecast for atleast the item.
 12. The computer program product of claim 11, whereinthe point-of-sale data in a first data format is received from at leastone of, a first brick-and-mortar store, a first kiosk, and a firstonline store.
 13. The computer program product of claim 11, wherein thedistribution-level order data in a second data format is received fromat least one of, a wholesaler, a distributor, and a distribution center.14. The computer program product of claim 11, further comprisingformatting the combined forecast into a third data format to generate areformatted combined forecast comprising an item quantity and a thirddate or third date range.
 15. The computer program product of claim 11,wherein the second data format is different from the first data format.16. The computer program product of claim 11, wherein the point-of-saledata in a first data format is received from a first entity and thedistribution-level order data in a second data format is received from asecond entity.
 17. The computer program product of claim 16, wherein thefirst entity is different from the second entity.
 18. A systemcomprising: a first incoming data processing module port to receivepoint-of-sale data in a first data format, the first point-of-sale datacomprising an item identifier and a first date or first date range; afirst incoming data processing module to determine a point-of-sale dataorigin using a first address identifier; a second incoming dataprocessing module port to receive distribution-level order data in asecond data format, the distribution-level order data comprising theitem identifier and a second date or second date range; a secondincoming data processing module to determine distribution-level orderdata origin data using a second address identifier; and a forecastingmodule to combine at least a portion of the point-of-sale data with atleast a portion of the distribution-level order data to generate acombined forecast for at least the item.
 19. The system of claim 18,wherein the point-of-sale data in a first data format is received from afirst entity and the distribution-level order data in a second dataformat is received from a second entity.
 20. The system of claim 19,wherein the first entity is different from the second entity.